code: def __init__(
      self,
      in_planes,
      out_planes,
      ksize,
      stride=1,
      pad=0,
      dilation=1,
      groups=1,
      has_bn=True,
      norm_layer=nn.BatchNorm,
      bn_eps=1e-5,
      has_relu=True,
      has_bias=False,
      **kwargs,
  ):
      super(ConvBnRelu, self).__init__()
      with self.name_scope():
          self.conv = nn.Conv2D(
              in_channels=in_planes,
              channels=out_planes,
              kernel_size=ksize,
              padding=pad,
              strides=stride,
              dilation=dilation,
              groups=groups,
              use_bias=has_bias,
          )
          self.has_bn = has_bn
          self.has_relu = has_relu
  
          if self.has_bn:
              self.bn = norm_layer(
                  in_channels=out_planes, epsilon=bn_eps
              )
          if self.has_relu:
              self.relu = nn.Activation("relu")
  
imports: from __future__ import division
  from mxnet.gluon import nn
  from mxnet.context import cpu
  from mxnet.gluon.nn import HybridBlock
  from gluoncv.model_zoo.segbase import SegBaseModel
  from gluoncv.model_zoo.pspnet import _PSPHead
label_desc: Use image node_id 1 to create a new ConvBnRelu object from inherited base classes: HybridBlock with example: obj = ConvBnRelu(in_planes, out_planes, ksize, stride, pad, dilation, groups, has_bn, norm_layer, bn_eps, has_relu, has_bias)